Research Article
Intelligent Power Controller of Wireless Body Area Networks Based on Deep Reinforcement Learning
@INPROCEEDINGS{10.1007/978-3-030-57115-3_21, author={Peng He and Zhenli Liu and Lei Fu and Zhongyuan Tao and Jia Liu and Tong Tang and Zhidu Li}, title={Intelligent Power Controller of Wireless Body Area Networks Based on Deep Reinforcement Learning}, proceedings={Bio-inspired Information and Communication Technologies. 12th EAI International Conference, BICT 2020, Shanghai, China, July 7-8, 2020, Proceedings}, proceedings_a={BICT}, year={2020}, month={8}, keywords={Wireless body area network Power controller Deep Q network}, doi={10.1007/978-3-030-57115-3_21} }- Peng He
Zhenli Liu
Lei Fu
Zhongyuan Tao
Jia Liu
Tong Tang
Zhidu Li
Year: 2020
Intelligent Power Controller of Wireless Body Area Networks Based on Deep Reinforcement Learning
BICT
Springer
DOI: 10.1007/978-3-030-57115-3_21
Abstract
Wireless Body Area networks allow groups of tiny sensors to communicate for purpose of medical applications. With the progress of sensor manufacture and artificial intelligence, abundant wearing devices are produced and applied with powerful intelligence functionalities. In wireless body area networks, battery energy capacity and inter-network interference are two serious threats to restrict the raise of performance. In this work, we focus on the power controlling theme in wireless body area networks. First, we introduce the primer overview of the deep-Q-Network algorithm, which is the method utilized in this work. Second, we present our communication system which is composed of two interfered WBANs. Third, we show how to design the power controller based on the deep-Q-network algorithm. The results reveal that our proposed power controller significantly decreases energy consumption by sacrificing little throughput performance.
